Methods for analyzing defect artifacts to precisely locate corresponding defects

ABSTRACT

Described are methods and systems for providing improved defect detection and analysis using infrared thermography. Test vectors heat features of a device under test to produce thermal characteristics useful in identifying defects. The test vectors are timed to enhance the thermal contrast between defects and the surrounding features, enabling IR imaging equipment to acquire improved thermographic images. In some embodiments, a combination of AC and DC test vectors maximize power transfer to expedite heating, and therefore testing. Mathematical transformations applied to the improved images further enhance defect detection and analysis. Some defects produce image artifacts, or &#34;defect artifacts,&#34; that obscure the defects, rendering difficult the task of defect location. Some embodiments employ defect-location algorithms that analyze defect artifacts to precisely locate corresponding defects.

This application is a division of application Ser. No. 10/370,206, filedFeb. 18, 2003, entitled, “METHODS FOR ANALYZING DEFECT ARTIFACTS TOPRECISELY LOCATE CORRESPONDING DEFECTS”, which is assigned to theassignee of the present application.

BACKGROUND

Electrical circuits, such as printed circuit boards (PCBs), integratedcircuits, and flat-panel displays (FPDs), may be tested for defectsusing infrared (IR) thermography. In general, power is applied to adevice under test (DUT) to heat various of the device features. Aninfrared detector then captures a test image of the heated DUT. Theresulting image, a collection of pixel-intensity values spatiallycorrelated to the imaged object, is then compared with a similarcollection of reference image data. Differences between the test andreference data, typically stored as a “composite image,” indicate thepresence of defects.

Defect identification algorithms analyze composite images toautomatically identify defects, and consequently improve throughput andquality in device manufacturing. Examples of such inspection systemsinclude, but are not limited to, inspection of FPDs, PCBs, MEMS-baseddevices, semiconductor devices, and biomedical specimen. One purpose ofsuch systems is to test for defects potentially present on a device atsome critical point during manufacture of that device. Once identified,the defects can then be repaired by a repair system, or a choice can bemade to reject the device, leading to manufacturing cost savings in bothcases. Other applications include inspection and identification ofartifact-like features in research specimens, e.g., in biology.

One particularly important use of IR thermography is the testing of theactive layer, or “active plate,” of liquid-crystal display (LCD) panels.Defect analysis can be used to improve processing and increasemanufacturing yield. Also important, defective panels can be repaired,provided the number and extent of defects are not too great, againincreasing manufacturing yield.

FIG. 1 (prior art) depicts portions of an active plate 100 for use withan LCD panel. (FIG. 1 was taken from U.S. Pat. No. 6,111,424 toBosacchi, which issued Aug. 29, 2000, and is incorporated herein byreference.) Active plate 100 includes a first shorting bar 105 connectedto each pixel in an array of pixels 110 via a collection of source lines115 and a second shorting bar 120 connected to each pixel 110 via acollection of gate lines (control lines) 125.

According to Bosacchi, active plate 100 is tested by evaluating the IRemission of active plate 100 with voltage applied to shorting bars 105and 120. With power thus applied, portions of plate 100 operate asresistive circuits, and consequently dissipate heat. The heatingresponse characteristics of plate 100 are then evaluated, preferablyafter plate 100 reaches a stable operating temperature (thermalequilibrium).

In the absence of defects, the pixel array should heat up uniformly.Non-uniform thermal characteristics, identified as aberrant IR intensityvalues, therefore indicate the presence of defects. Reference intensityvalues can be obtained by averaging the pixel intensity values of agiven image frame, or by means of a reference frame corresponding to anideal or defect-free reference plate.

FIG. 2 (prior art) details a portion of a conventional pixel 110, and isused here to illustrate a number of potential defects. The depictedfeatures of pixel 110 are associated with the active plate of aliquid-crystal display, and include a thin-film transistor 200 having afirst current-handling terminal connected to one of source lines 115, acontrol terminal connected to one of gate lines 125, and a secondcurrent-handling terminal connected to a capacitor 210. The secondelectrode of capacitor 210 connects to a common line 212. Pixel 110 alsoincludes a second capacitor 211 having a liquid-crystal dielectric.

The defects, which are illustrative and not exhaustive, include bothshorts and opens. The shorts are between: source line 115 and gate line125 (short 215) or common line 212 (short 216); the two current-handlingterminals of transistor 200 (short 220); the gate and secondcurrent-handling terminal of transistor 200 (short 225); and the twoterminals of capacitor 210 (short 226). The opens segment the source,gate, and common lines (opens 227, 228, and 229), and are between:source line 115 and transistor 200 (open 230), gate line 125 and thecontrol terminal of transistor 200 (open 232), capacitor 210 and commonline 212 (open 235), and transistor 200 and capacitor 210 (open 233).

Each defect of FIG. 2, plus a number of others, adversely impacts theoperation of pixel 110. Unfortunately, many of these defects aredifficult to discover using conventional test methods. There istherefore a need for improved methods and systems for identifying andlocating defects.

Some inspection systems include an excitation source that excites theobject under test in a way that highlights defects to an imaging system.The type of excitation depends upon the imaging system, which mayacquire images based on visible light, infrared, combined spectroscopy,magnetic fields, etc. Whatever imaging system is employed, test imagesof the object under test are contrasted with some reference image toobtain a composite image: significant differences between the test andreference images show up in the composite image, and identify potentialdefects.

Some forms of excitation produce defect artifacts, which are differencesbetween test and reference images that are caused by defects but that donot physically correlate to defect areas. A short between two linesincreases current through those lines, and consequently elevates thetemperatures of the lines along with the short. Thus the lines, thoughnot themselves defective, nevertheless appear with the short in thecomposite image. The defect data representing the short is thus imbeddedwithin defect-artifact data (i.e., a “defect artifact”). Defectartifacts often obscure the associated defects, rendering them difficultto precisely locate. Human operators can locate a defect within a defectartifact by careful study under a microscope, but people are relativelyslow and are quickly fatigued. There is therefore a need for means ofautomatically distinguishing defects from their related artifacts.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 (prior art) depicts portions of an active plate 100 for use withan LCD panel.

FIG. 2 (prior art) details an exemplary pixel 110, and is used here toillustrate a number of potential defects.

FIG. 3 depicts a test configuration 300, including a conventional panel305 and an inspection system adapted in accordance with an embodiment ofthe invention.

FIG. 4 depicts a portion of a panel 400 adapted in accordance with oneembodiment to provide enhanced testability.

FIG. 5 depicts a portion of an LCD panel 500 adapted in accordance withone embodiment.

FIG. 6A is a diagram 600 illustrating the thermal response of theabove-described sample defect and the surrounding area.

FIG. 6B depicts a diagram 630 illustrating the thermal response ofsample defect 610.

FIG. 6C depicts an experimentally obtained image 680 highlighting a line685 indicative of an open.

FIG. 7 depicts a composite image 700 showing three representativedefects, a point-type defect 705, a line-type defect 707, and acorner-type defect 710.

FIG. 8 is a composite image 800 exhibiting a point-type defect 805, aline-type defect 810, and a corner-type defect 815.

FIG. 9 is a flowchart 900 depicting a defect-location algorithm 900 inaccordance with one embodiment.

FIG. 10 depicts an embodiment of MFA 910 of FIG. 9.

FIG. 11 depicts an embodiment of type-specific MFA 935 of FIG. 9 adaptedfor use with line-type defect artifacts.

FIG. 12 depicts an embodiment of defect-location algorithm (DLA) 945 ofFIG. 9.

FIG. 13 depicts an array of LDF structures 1205 in accordance with oneembodiment.

FIG. 14 depicts an illustrative filtered composite image 1400 similar towhat one might expect from step 907 of FIG. 9.

FIGS. 15A-15D depict binary, type-specific images 1505, 1510, and 1515like images 940[1 . . . i] of FIG. 9.

FIG. 16 depicts a tree 1600, a compilation of the information in keys1525, 1530, and 1535 of FIGS. 15B-D.

FIG. 17 is an illustrative peak profile 1700 showing the relationshipbetween four defect artifacts 1705, 1710, 1715, and 1720.

DETAILED DESCRIPTION

FIG. 3 depicts a test configuration 300, including a conventional panel305 and an inspection system 310 adapted in accordance with anembodiment of the invention. Panel 305 is similar to panel 100 of FIGS.1 and 2, like-numbered elements being the same or similar. Panel 305includes a shorting bar 312 that is not depicted in FIG. 1, but isnevertheless conventional. Inspection system 310 includes an IR detector315 (e.g., an IR camera) oriented over panel 305 to provide image datato a computer 320 via a frame grabber 325. An excitation source, signalgenerator 330, provides electrical test signals, or “test vectors,” topanel 310. The test vectors heat features of panel 310 to producethermal characteristics useful in identifying defects.

Computer 320 controls signal generator 330 to apply test vectors topanel 305. These test vectors enhance the thermal contrast betweendefects and the surrounding features, and consequently allow IR detector315 to acquire improved thermographic images for defect detection andanalysis. Computer 320 additionally instructs IR detector 315 when toacquire image data, receives and processes captured test-image data fromframe grabber 325, and provides a user interface (not shown).

IR detector 315 should have excellent temperature sensitivity. In oneembodiment, detector 315 is an IR Focal-Plane Array Thermal ImagingCamera employing a 256×320 element InSb (Indium Antimonide) detector.The minimum temperature sensitivity of this camera is less than 0.020degrees C. Some embodiments include multiple IR detectors, for example arelatively low magnification IR camera for defect detection and a highermagnification IR camera for defect detection and analysis. Additionalcameras might also be used to increase inspection area, and thusinspection bandwidth.

Signal generator 330 provides a source test vector V_(TS) to shortingbar 105, a gate test vector V_(TG) to shorting bar 120, and a commontest vector V_(TC) to shorting bar 312. Referring back to FIG. 2,testing for some types of defects (e.g., opens 230, 233, and 235)requires transistor 200 be turned on to create a signal path betweenrespective source and common lines 115 and 212. Signal generator 330thus applies a DC test vector V_(TG) to gate line 125 (via shorting bar120), thus turning transistor 200 on, while applying the source andcommon test vectors V_(TS) and V_(TC).

Even with transistor 200 forward biased, a non-defective pixel 110 willnot pass direct current, absent short 226, because capacitor 210 blocksdirect current. The source and common test vectors V_(TS) and V_(TC) aretherefore selected to produce an AC signal that passes through capacitor210. The frequency of the AC signal is matched to the impedance of theload provided by panel 305 to maximize power transfer to panel 305,which expedites heating, and therefore testing. Maximizing powertransfer also allows for testing with lower applied voltages, which areless likely to damage sensitive components. Also important, as detailedbelow, a combination of faster heating and specific image-capture timingprovides improved thermal contrast. In one embodiment, source testvector V_(TS) oscillates from zero to 30 volts at about 70 KHz andcommon test vector V_(TC) is ground potential.

Some embodiments apply either AC or DC test vectors between source line115 and common line 212, source line 115 and gate line 125, and betweengate line 125 and common line 212. Still other embodiment employ ACsignals to turn transistor 200 on. The simultaneous application of ACand DC test vectors, as detailed above, facilitates more comprehensivetesting than is obtained by application of only one type of waveform(e.g., only DC, AC, or pulsed DC test vectors).

FIG. 4 depicts a portion of a panel 400 adapted in accordance with oneembodiment to provide enhanced testability. Panel 400 conventionallyincludes an array of pixels 405, each connected to a source line 410, agate line 415, and a common line 420. Four sets of shorting bars (sourcebars 425, gate bars 435, and common bars 430) allow inspection systems,such as that of FIG. 3, to test subsets of pixels 405. Alternatively,four sets of one type of bar (e.g., four source bars or four gate bars)can be used to energize selected columns or rows. Energizing somefeatures while leaving adjacent features de-energized can improve imagecontrast. In other embodiments, only one or two types of shorting barsare provided in sets. There may only be one gate bar 435 and one commonbar 430, for example, in which case pixels 405 can be excited in foursets of columns. Moreover, one or more sets of shorting bars can includemore or fewer than four shorting bars.

FIG. 5 depicts a portion of an LCD panel 500 adapted in accordance withanother embodiment. Panel 500 conventionally includes an array of pixels505, each connected to a source line 510, a gate line 515, and a commonline 520. Source bars 525, gate bars 530, and common bars 535 aresegmented to allow an inspection system to power test areas (e.g., area540) one at a time. Alternatively, fewer types of bars need besegmented. For example, the common bar need not be segmented to powerarea 540 if the source and gate bars are segmented. Area 540 might becoextensive with the field of view of the IR detector used to captureimages. The number of pixels 505 in a given area is generally muchgreater than depicted in this simple example.

FIG. 6A is a diagram 600 illustrating the thermal response of anillustrative sample defect and the surrounding area. The sample defectis assumed to be a short having a resistance R of about twenty-fivethousand ohms, a volume V of the combined defect and associatedelectrodes of about 10⁻¹² m³, and an exposed surface area A of about10⁻⁵ m². The specific heat Cp of the electrodes is assumed to be about2.44×10⁶ J/m³K, and the convection heat transfer coefficient of thesurrounding air h_(air) is about 10 W/m²K. For an applied power of about6 milliwatts the equilibrium temperature at the defect location is about6.5 degree C. above the initial temperature. The following heat transfermodel illustrates the thermal response of the sample defect in responseto applied power: $\begin{matrix}{{{VC}_{p}\frac{\mathbb{d}{T(t)}}{\mathbb{d}t}} = {{P_{applied}(t)} - {h_{air}{A\left( {{T(t)} - T_{air}} \right)}}}} & (1)\end{matrix}$where:

-   -   1. V is the volume of the combined defect and associated        electrodes;    -   2. C_(p) is the average specific heat of the defect and        associated electrodes;    -   3. T(t) is temperature, in Kelvin, of the defect over time, in        seconds;    -   4. P_(applied)(t) is the applied power excitation over time, in        seconds;    -   5. h_(air) is the convection heat transfer coefficient of the        surrounding air;    -   6. A is the exposed surface area of the combined defect and        associated electrodes; and    -   7. T_(air) is the temperature of the surrounding air or initial        temperature (e.g., about 300K).        Equation (1) means, in essence, that the power applied to the        defect area is, at a given instant, equal to the sum of the        power absorbed by the defect area and the power dissipated into        the surrounding environment. Initially, when the temperature        difference between the defect area and surrounding environment        is minimal, the first addend dominates the equation. The second        addend gains influence as the temperature of the defect area        rises.

A diagram 605 illustrates the sample defect area 610 and surroundingarea 615 as a collection of boxes, each box representing the imageintensity recorded by an image pixel. For ease of illustration, diagram605 illustrates defect area 610 as a single pixel. Diagram 600 assumesabout six milliwatts is applied between the source and gate lines for atime sufficient for defect area 610—a short between the source and gatelines—to rise from an initial thermal equilibrium temperature T_(I) ofabout 300K to a final equilibrium temperature TE_(F) of about 306.5K.Under the specified conditions, traversing this temperature window takesabout 0.1-0.2 seconds for a typical active plate.

A first response curve 620 illustrates the thermal response of defectarea 610. The vertical axis of diagram 600 represents temperature as apercentage of the temperature span between initial temperature T_(I) andfinal equilibrium temperature TE_(F) of response curve 620. Thehorizontal axis represents time, in thermal time constants τ. Thethermal time constant τ is the time required for the temperature ofdefect area 610 to rise 63.2% of the way from a given temperature tofinal equilibrium temperature TE_(F). For practical purposes, the defecttemperature is at the final equilibrium temperature TE_(F) after four orfive time constants τ.

The thermal response of area 615 surrounding defect area 610 differsfrom the thermal response of defect area 610. If the defect is a short,heat from area 610 diffuses into area 615, causing the temperature ofarea 615 to rise with defect 610. The rising temperature of area 615lags that of defect 610, however, and rises to a lower final thermalequilibrium temperature than defect 610. Test vectors applied inaccordance with some embodiments provide increased temperature contrastbetween areas 610 and 615 to allow IR imaging systems to more easilyresolve defect features. IR inspection systems then employ uniqueimage-acquisition timing to capture test image data well before defectarea 610 reaches final equilibrium temperature TE_(F). (If defect area610 is an open, the temperature of area 610 lags surrounding area 615,but nevertheless eventually reaches a final equilibrium temperature.)

FIG. 6B includes a diagram 630 illustrating test vectors andimage-acquisition timing that enhance thermal contrast between defectsand the surrounding areas. Diagram 630 includes a thermal response curve640 representing the repetitive heating and cooling of defect area 610in response to test vectors. FIG. 6B additionally includes a pair ofwaveforms IMAGE and EXCITE that share a common time scale with diagram630. The high portions of waveform IMAGE represent windows of timeduring which IR images of areas 610 and 615 are captured. One or morereference images are captured during each reference window 645, whileone or more test images are captured during each test window 650. Thehigh portions 655 of waveform EXCITE represent times during which testvectors are applied to defect 610 to introduce thermal contrast betweendefect 610 and surrounding area 615.

To capture test images, an inspection system (e.g. inspection system 310of FIG. 3) applies test vectors to a device under test during times 655.The inspection system then captures one or more IR images of the defectarea well before the features of interest reach the final thermalequilibrium temperature TE_(F).

It is desirable to keep the maximum temperature (i.e., the peaks ofresponse curve 640) below 95% of the difference between the initialtemperature T_(I) and the final equilibrium temperature TE_(F). In somecases, the maximum temperature may even endanger DUT functionality. Theoptimal upper limit for temperature thus varies for different DUTs, testprocedures, etc., but is preferably less than 86.5% in many cases. Ourexperimental data suggest that excellent results are obtained when themaximum temperature does not exceed about 63.5% of the differencebetween the initial temperature T_(I) and the final equilibriumtemperature TE_(F) (i.e., before the passage of one time constant). Theheating/imaging steps are repeated a number of times, and the resultsaveraged or otherwise combined, to reduce the effects of noise. Themaximum and minimum peaks of response curve 640 should be sufficientlyspaced for the selected detector to resolve the temperature difference.

Defect 610 can be heated to higher temperatures, for more than three orfour time constants, for example; in such cases, the images can still betaken well before defect 610 reaches the final equilibrium temperatureTE_(F). Because heating takes time, selecting relatively low maximumtemperatures speeds testing. Moreover, the test voltages selected tomaximize image contrast may, if applied too long, raise the temperaturesof areas 610 and 615 high enough to damage sensitive components. In suchcases, the test vectors are applied long enough to achieve a desiredlevel of thermal contrast without raising the temperatures of areas 610and 615 above some maximum temperature.

The applied test vectors differ depending in part on the DUT. In oneembodiment suitable for testing an active panel from a liquid-crystaldisplay (LCD) having SXGA resolution, the low portions of the EXITEwaveform represent periods of about 20 ms when no test vectors areapplied, and the high portions 655 represent periods of about 80 msduring which time AC source and common test vectors oscillate from zeroto 30 volts at about 70 KHz.

Conventional infrared imaging systems represent images using arrays ofnumbers, each number representing a pixel intensity value. Each pixelintensity value, in turn, represents the image intensity of acorresponding area of the DUT. In inspection system 310 of FIG. 3, forexample, frame grabber 325 delivers to computer 320 an array of pixelintensity values for each captured image.

Some conventional inspection systems average sequences of images toreduce the effects of noise. Averaged test images are then contrastedwith a reference image to identify differences, which indicate thepresence of defects. The above-described methods and systems can be usedto produce enhanced test and reference images.

One embodiment employs an image transformation in place of conventionalaveraging. The image transformation, defined below in equation 2, isapplied to both a sequence of test images and a sequence of referenceimages. The resulting transformed test and reference images I_(T) andI_(R) are then contrasted to identify differences. $\begin{matrix}{I = {D\left( {L\left( {F\left( \frac{\sum\limits_{i = 0}^{n - 1}{D^{- 1}\left( I^{i} \right)}}{n} \right)} \right)} \right)}} & (2)\end{matrix}$where:

-   -   1. D⁻¹ represents an image transformation (casting) from        sixteen-bit numbers provided by frame grabber 325, each number        representing the intensity of one pixel, to their floating-point        inverses (quasi-inverse to D, below);    -   2. I^(i) is the i-th image of the sequence;    -   3. n is the number of images in the sequence;    -   4. F represents image filtering, e.g., low-pass filtering to        reduce noise and approximate data provided by defective pixels        in IR detector 315;    -   5. L is the application of a look-up table to the image to        translate the range of intensity values to a different scale,        e.g., a different range or from linear to quadratic; and    -   6. D is an image transformation (casting) from floating-point        values to sixteen-bit numbers.

In carrying out the image transformation of equation 2 on a sequence ofimages (test or reference), each pixel intensity value in each of thesequence of images is converted to a floating-point number. Theresulting image arrays are then averaged, on a per-pixel basis, tocombine the images into a single image array. Next, the resulting imagearray is filtered to reduce the effects of noise and to approximate dataassociated with defective pixels in the imaging device. Each datumassociated with a defective pixel, identified by an extreme intensityvalue, is replaced with a new intensity value interpolated from datarepresentative of neighboring areas.

The intensity values of the combined, filtered image array are appliedto a look-up table that translates the range of intensity values to adifferent scale, e.g., a different range or from linear to quadratic.Finally, the values in the resulting translated image array areconverted back from floating-point numbers to digital numbers to producethe transformed image I.

The image transformation of equation 2 is applied to a series of testimages and a series of reference image to produce respective combinedtest and reference images I_(T) and I_(R). The test and reference imagesI_(T) and I_(R) are then contrasted, using well-known image processingtechniques, to produce a composite image. The composite image highlightstemperature differences between the test and reference images;unexpectedly warm or cool areas are indicative of defects.

In general, short circuits produce relatively high currents, andconsequently grow relatively hot. Open circuits reduce current, remainrelatively cool, and are therefore more difficult to image using IRthermography. The improved thermal contrast provided by the foregoingembodiments allows sensitive IR detectors to capture images thathighlight many types of defects previously difficult or impossible toview with conventional IR thermography. Such defects include opencircuits of the type depicted in FIG. 2. The image transformation ofequation 2 further enhances the results.

FIG. 6C depicts an experimentally obtained image 680 illustrating howthe above-described embodiments produce sufficient thermal contrast tohighlight opens. A line 685 represents a relatively cool defect artifactresulting from the presence of an open.

Defect-Location Algorithms

A short between two lines increases current through those lines, andconsequently elevates the temperatures of the lines. Thus the lines,though not themselves defective, may nevertheless appear in thecomposite image. The portion of the composite image highlighting thelines is termed the “defect artifact” of the short. Opens block current,and consequently reduce the temperature of associated features. Thesefeatures then appear in the composite image as a “defect artifact” ofthe open. (FIG. 6C depicts a line-type defect artifact associated withan open). Test images thus include defect data spatially correlated tothe defect region and defect-artifact data spatially correlated todefect-free regions of the imaged object. Unfortunately, defect-artifactdata can obscure defect data during image analysis, rendering itdifficult to precisely locate defects. Image-processing algorithms inaccordance with some embodiments analyze the defect data anddefect-artifact data to address this problem. (In the context of images,a related collection of defect data may be referred to as a “defect,”for brevity; likewise, defect-artifact data may be termed “defectartifacts.” Whether the term “defect” or “defect artifact” refers to aphysical feature of an imaged object or image-data representative of aphysical feature will be clear from the context.)

FIG. 7 depicts a composite image 700 showing three representativedefects, a point-type defect 705, a line-type defect 707, and acorner-type defect 710. These defects are assumed to be of similar size,but the defect images nevertheless differ due to their respective defectartifacts 715, 720, and 725. Unfortunately, actual composite IR imagesdo not so clearly distinguish defects from defect artifacts, renderingdifficult the task of precisely locating defects. FIG. 8, though notbased on measured data, more accurately depicts a composite image 800illustrating how a point-type defect 805, a line-type defect 810, andcorner-type defect 815 might appear in a composite image. The defectartifacts obscure the location of the defects. One embodiment addressesthis problem, distinguishing defects from defect artifacts to facilitatedefect detection, location, and analysis.

Image processing to distinguish a defect from the associate artifact isbased, in part, on classifying defect-artifact data. Processing differs,for example, for point-type, line-type, and corner-type defects.Embodiments of the invention therefore include image-processingtechniques that sort defect artifacts by type. Image-processingtechniques that classify artifacts by type include pattern recognitionand morphological analysis. The “Handbook of Image and VideoProcessing,” edited by Al Bovik (2000) and “Nonlinear Filters for ImageProcessing,” edited by E. Dougherty and J. Astola (1999) describe imageprocessing and mathematical morphology known to those of skill in theart and suitable for use in some embodiments: these texts areincorporated herein by reference.

FIG. 9 is a flowchart depicting a defect-location algorithm 900 inaccordance with one embodiment. Algorithm 900 receives a composite image905, optionally filtered using a fast-Fourier-transform (FFT) low-passfilter (step 907), in which defect data is bounded by defect-artifactdata. Subsequent processing automatically locates the defect data withinthe defect artifacts to produce a list of defect coordinates. In oneembodiment, composite image 905 is an IR composite image of the typediscussed above; however, many other types of images depict subjects ofinterest surrounded by artifacts of those subjects. Algorithms inaccordance with the invention can be used to localize such subjects. Forexample, images obtained from visual optics and/or nuclear-typeexperiments depict subjects and subject-artifacts.

The above-referenced Bovik reference describes a connection betweengray-level and binary morphology that is used in some embodiments todistinguish defects from their associated artifacts. This connection isbased on the observation that an image I(x),X ε D can be reconstructedfrom the set of thresholds. Such reconstruction may be expressedmathematically as follows:Θ_(v)(I)={x ε D:I(x)≧v},−∞<v<∞  (3)where Θ_(v)(I) is the threshold of a grayscale composite image I with athreshold level v, and D ⊂

² is the domain of the image:I(x)=sup_(v ε R) {x ε Θ _(v)(I)}  (4)Algorithm 900 employs a similar type of image reconstruction to defineand optimize threshold levels to yield improved defect localization.

A morphological filtering algorithm (MFA) 910 is applied to the filteredcomposite image for each of a number of threshold levels 915. Therepetition of MFA 910 for each threshold level, illustrated by boundingMFA 910 within a for-loop 920A and 920B, produces a set of i thresholdedcomposite images 925[1 . . . i]. In each image 925, all pixel values ofthe filtered composite image greater than or equal to the appliedthreshold level are expressed using one logic level (e.g., logic one)and all pixel values less than threshold level 915 are expressed using asecond logic level (e.g., logic zero). In one embodiment, thresholdlevels 915 are in units of standard deviation of pixel-intensity valuesfrom a filtered composite image produced by filter 907. Histogramanalysis is provided to calculate mean μ and standard deviation σ; theactual level of threshold is equal to μ+σ·T, where T is the inputthreshold level in units of σ. MFA 910 is detailed below in connectionwith FIG. 10.

The next for-loop (930A and 930B) treats each image 925 to a second MFA935, this one type-specific. For line-type artifacts, for example, MFA935 removes other types of defect artifacts (e.g., corner- andpoint-type artifacts), leaving only lines. For-loop 930 produces a setof i images, each having j line-type artifacts. In the illustration, thetop image 940[1] includes two line-type defects: the point- andcorner-type defects of image 925[1] are removed. The remaining images940[2 . . . i] may have more or fewer artifacts. MFA 935 is detailedbelow in connection with FIG. 11.

In a final sequence of operations, a defect-location algorithm DLA 945analyzes the defect artifacts in images 940 to precisely identify thedefect coordinates 950 from among the defect artifacts. A version of DLA945 specific to line-type defects is detailed below in connection withFIG. 12.

FIG. 10 depicts an embodiment of MFA 910 of FIG. 9, which is repeatedlyapplied to composite image 905 to produce a sequence of i filteredimages 925. First, one of threshold levels 915 is applied to theFFT-filtered composite image 905 (step 1000). A morphological closingstep 1005 (a dilation followed by an erosion) applied to the product ofstep 1000 smoothes defect artifacts, and a subsequent filter removessmall image effects induced by noise or defective detector pixels (step1010). MFA 910 thus produces one of the i images 925[1 . . . i]. MFA 910is repeated, as shown in FIG. 9, for each one of threshold levels 915.

FIG. 11 depicts an embodiment of type-specific MFA 935 of FIG. 9 adaptedfor use with line-type defect artifacts. MFA 935 receives filteredimages 925 from MFA 910 and employs type-specific filtering operationsthat remove any artifacts other than line-type artifacts. Step 1110provides type-specific morphological operations based on an artifact'sposition on the image rather than its geometrical properties. Forexample, an artifact located in areas where that type of artifact is notexpected may be eliminated. Any holes in the surviving artifacts arethen filled (step 1115) using any of a number of conventionalhole-filling techniques.

Next, step 1120 filters the images from step 1115 using a type-dependentlist of constraints 1125 derived from artifact type and areainformation. Constraints 1125 are a type-specific list of measurableartifact parameters and ranges. For example, line-type artifacts insimple cases may be filtered out by their circular factor (“lines” arenot circular), orientation (e.g., line-type artifacts are close tovertical or horizontal), and their edge intersections (e.g., line-typedefects, distinct from corner-type defects, do not intersect twoadjacent image boundaries). The resulting filtered binary image 940contains only the desired type of artifact. In one embodiment, image 940has two pixel values: background (0) and artifact (1). The artifactsappear as areas of touching pixels, all set to 1; the surrounding areasappear as pixels set to 0. The details of the implementation of eachblock of MFA 935 are well known to those of skill in the art of imageprocessing. MFA 935 is repeated, as shown in FIG. 9, for each one ofthreshold levels 915.

The set of morphological operations applied in MFA 935 is specific toline-type defects in the example, but these operations can be modifiedas desired to accommodate e.g. point- and corner-type defects. If onlypoint-type artifacts were of interest, MFA 935 can be adapted to removethe artifacts that touch the border of the image, artifacts greater thana specified maximum area, etc., which would correspond to line-,corner-, and other type artifacts. If only corner-type artifacts were ofinterest, MFA 935 can be adapted to remove artifacts that do notrepresent intersecting perpendicular lines of a specified minimum area,etc. MFA 935 can be modified to select these and other types ofartifacts using well-known image-processing techniques.

FIG. 12 depicts an embodiment of defect-location algorithm (DLA) 945 ofFIG. 9, which generates a list of physical coordinates for defects on anobject under test using the array 940 of type-specific images. DLA 945is specific to line-type defect artifacts, but can be adapted for usewith other types of defects, as will be evident to those of skill in theart.

In the first step, a line-defect filter LDF algorithm 1200 produces anarray of i LDF structures 1205[1 . . . i], one LDF structure for eachimage 940. In addition to type-specific images 940, LDF algorithm 1200receives as inputs 1208 the original filtered composite image (from step907 of FIG. 9), the value of the initial threshold level used to createimages 940, a threshold step value indicative of the difference betweenthreshold values used to acquire successive images, and the number i ofthreshold levels (the number of threshold levels i is the same as thenumber of images 940 because 940 are thresholded images).

FIG. 13 depicts a linked-list array of LDF structures 1205 (FIG. 12) inaccordance with one embodiment. In this example, four threshold levelsand associated processing are used to produce four LDF structures1205[1] through 1205[4], though the actual number of arrays can be moreor fewer. These arrays are adapted for use with line-type defectartifacts, but can be modified for use with other types. Each LDFstructure 1205[i] includes the below-listed features.

-   -   1. A threshold field 1300 that stores the threshold level        applied to the composite image to produce the respective binary        image 940[i].    -   2. A number field 1305 that stores the number n of identified        defects, two in the example of FIG. 12.    -   3. An image field 1310 that stores the respective binary image        940[i].    -   4. An array 1315 of defect structures 1320[1-j], where j is the        number of defect artifacts in the respective image 940[i]. Each        defect structure 1320 stores the X and Y coordinates of the        pixel at the tip, or “peak,” of the respective line-type defect        artifact (defects are assumed to be near the tip of line-type        defect artifacts). Each defect structure additionally includes a        pixel-value field 1325 that stores the pixel intensity value        corresponding to the X and Y coordinates of the peak pixel in        composite image 905.    -   5. An array 1330 of line structures 1335[1-j], each associated        with a defect artifact in the respective filtered image 940[i].        Each line structure 1335 includes an area field 1340 that stores        the area of the defect artifact (in pixels); a rectangle field        1345 that stores the left, top, right, and bottom coordinates of        a rectangle encompassing the defect artifact; a belongs-to field        1350 that stores a line index relating a line-type artifact to        features of an image taken at a lower threshold level (−1 of no        such line exists); and a contains field 1355 that stores an        array of line indices relating a line-type defect in the        filtered image with one or more related lines in another        filtered image taken at a higher threshold level. The purposes        of Belongs-to field 1350 and contains field 1355 are detailed        below in connection with FIGS. 14 and 15A-D.

Returning to FIG. 12, a LDF structures 1205[1 . . . i] are analyzed(step 1210) to develop j LDF gradient peak profiles 1215[1 . . . j], onefor each type-specific defect artifact in each image 940. The followingdiscussion of FIGS. 14, 15A-15D, and 16 illustrates the process of peakprofiling 1210.

FIG. 14 depicts an illustrative filtered composite image 1400 similar towhat one might expect from step 907 of FIG. 9. Image 1400 includes apair of vertical line-type defect artifacts A1 and A2 and a point-typeartifact A3. The boundaries of the defect artifacts are blurred todepict the lack of an emphatic image boundary between defect- anddefect-artifact data. It is assumed, however, that defects associatedwith line-type defect artifacts are near artifact tips, or “peaks,” anddefects associated with point-type defects are centered within therelated artifact.

FIGS. 15A-15D depict binary, type-specific images 1505, 1510, and 1515like images 940[1 . . . i] of FIG. 9. Each image represents compositeimage 1400 with a distinct applied threshold value, per MFA 910;point-type artifact A3 is removed from each binary image by thesubsequent application of type-specific MFA 935. Each image is stored infield 1310 of an LDF structure 1205 (FIG. 13) along with the otherimage- and artifact-specific information described above in connectionwith FIG. 13.

Image 1500 (FIG. 15A) includes a pair of line artifacts LA1 and LA2. Arelated key 1520 identifies artifacts LA1 and LA2 as corresponding torespective artifacts A1 and A2 of FIG. 14. As threshold levels increase,line-type artifacts become smaller and thinner, and may disappear orsplit into several disconnected lines. In FIG. 15B, for example, thedefect artifact LA2 depicted as a single line in FIG. 15A appears as apair of line artifacts LB2 and LB3. A key 1525 identifies artifact LB1as belonging to artifact LA1 of image 1500 and artifacts LB2 and LB3 asbelonging to artifact LA2 of image 1500. This artifact “ownership” isrecorded in the “belongs to” field 1350 of the LDF array associated withimage 1505; similarly, the “contains” field 1355 of the LDF arrayassociated with image 1500 notes that artifact LA1 “contains” artifactLB1 and artifact LA2 contains artifacts LB2 and LB3. FIGS. 15C and 15Dinclude different collections of defect artifacts and respective keys1530 and 1535 illustrating the “belongs to” relationship betweenartifacts in the various images.

FIG. 16 depicts a tree 1600, a compilation of the information in keys1525, 1530, and 1535 of FIGS. 15B-D. Tree 1600 illustrates therelationship between defect artifacts A1 and A2 and the line-typeartifacts in binary images taken using successively greater thresholdlevels. For example, artifact LD2 of image 1515 belongs to artifact LC3of image 1510, which belongs to artifact LB3 of image 1505, whichbelongs to artifact LA2 of image 1500. Likewise, artifact LD1 of image1515 belongs to artifact LC1 of image 1510, which belongs to artifactLB1 of image 1505, which belongs to artifact LA1 of image 1500.

Returning briefly to FIG. 12, LDF gradient peak profiling step 1210 usesthe data depicted in FIGS. 15A-15D to produce j peak profiles 1215[1 . .. j], one profile for each defect. FIG. 17 is an illustrative peakprofile 1700 showing the relationship between four defect artifacts1705, 1710, 1715, and 1720, all of which relate to a common defectartifact. Profile 1700 expresses a relationship between the thresholdlevel and the placement of the peak pixel in each defect artifact alongthe y image axis. A plot 1725 of Pixel intensity along a slice of pixelsin parallel with a bisecting the line-type defect artifact of agrayscale image illustrates how pixel intensity generally increases asthe defect artifact is encountered. Artifacts 1705, 1710, 1715, and 1720represent slices of plot 1725 taken at four threshold levels. The bold“+” signs indicate the y locations of the peak (tip) pixels of therespective defect artifacts for different threshold levels.

Returning to FIG. 12, locating step 1220 extracts from the LDF arraysdata of the type represented in FIG. 17 to produce a number oftwo-dimensional arrays, each with first dimension DIMi (the number ofthreshold levels) and second dimension DIMj (the number of artifacts inthe image thresholded at the lowest level, it is the size of array 1320of LDF structure 1205[1]). These arrays are as follows (the formaldefinitions are given below in pseudocodes:

-   -   1. y[j, i] is y-coordinate of defect structure 1320[j] of LDF        structure 1205[i], it specifies the defect related to the j-th        artifact of the image 1310 thresholded at the level i;    -   2. Line[j, i] is the index of the line that contains defect j at        threshold level i (pseudocodes explain how the index is chosen;        and    -   3. dy[j, i] is the difference between y values of defects        1320[j] from LDF structures 1205[i+1] and 1205[i].

For every artifact j, step 1210 performs the following initializationand loop:

-   Initialization: (for every j=1, . . . , Dimj):    -   Line[j, 1]=j;    -   y[j, 1]=LDF_Array[1].Defects[j].y        At threshold level #1 (initial threshold) all filtered lines are        assumed to contain defects, so Line[j, 1] is the index of the        line and y[j, 1] is the assumed y position of the defect        associated with this line.-   Iteration i: i=1, . . . , DIMi−1 (for every j)    /* i-threshold index; j-index of the line 1335 from LDF structure    1205[1] corresponding to the lowest threshold level. */    -   1. k_(opt)=argmin_(k){        -   LDF_Array[i+1].        -   Defects[            -   LDF_Array[i].Lines[Line[j, i]].Contains[        -   k]].y,        -   }    -   2. Line[j, i+1]=LDF_Array[i].Lines[Line[j, i]]. Contains        [k_(opt)]    -   3. y[j, i+1]=LDF_Array[i+1].Defects[Line[j, i+1]].y    -   4. dy[j, i]=y[j, i+1]−y[j, i]

In steps 1 and 2 above, for a chosen line[j, i] of threshold level i,the line for the next threshold level (i+1) belongs to line[j, i]. Amongthe lines of the threshold level (i+1) belonging to line[j, i], the linewith the lowest y value is chosen as the line most likely to include thedefect (the y axis of each image goes from top to bottom, so the lowesty value represents the highest point). After choosing the line Line[j,i] with the lowest y value, the y value for the defect associated withthis line is taken from the corresponding line structure. The valuedy[j, i] defined at step 4 of the foregoing pseudocode is used below todefine the LDF gradient

When line defects have been presented by trees of artifacts, as depictedin FIG. 16, and LDF gradient peak profiling has been made, the ends ofthe lines associated with defects (upper ends according to assumptionmade above) are defined as maxima of LDF gradients. The LDF gradient foreach threshold level index i is defined by $\begin{matrix}{{{LDFGrad}\left\lbrack {j,i} \right\rbrack} = \frac{D_{Th}}{{dy}\left\lbrack {j,i} \right\rbrack}} & (5)\end{matrix}$where j is the defect index, i is the threshold level index, and D_(Th)is the separation between threshold levels. D_(Th) is a constant, so thefollowing equality holds: $\begin{matrix}{{\max_{i}{{LDFGrad}\left\lbrack {j,i} \right\rbrack}} = \frac{D_{Th}}{\min_{i}{{dy}\left\lbrack {j,i} \right\rbrack}}} & (6)\end{matrix}$Further, because only the location of the maxima of LDF Gradients isused, the task of finding the maxima of LDF gradients is equivalent tosearching for the minimal elements in the rows of array dy[ ] calculatedby the LDF gradient peak profile algorithm 1210. In many cases theminimal dy is zero because the image location is measured in numbers ofpixels, and with this level of granularity the y-location of the defectmay be the same for neighboring threshold levels.

Sometimes there are several locations of minimal dy. The multitude oflocations with maximal LDF gradients (minimal dy) requires calculationof the first and the last indexes of the minimal elements in the rows ofarray dy[ ]. The data structure of the type maxLDFGrad contains thefollowing elements:

-   -   1. Threshold is the threshold level of the maximal LDF gradient;    -   2. Th. Ind. is index i of the threshold level;    -   3. Line Ind. is the line index j associated with the defect at        this threshold level;    -   4. DY is the minimal value of the row of array y[ ]; and    -   5. x and y are the coordinates of defect location.

Step 1220 employs an analysis of LDF gradient peak profiles 1215 tolocate the defects associated with the line-type defect artifactsspecified in the line structures of the LDF array. Step 1220 performs acalculation based on First and Last maxLDFGrad structures (input data1225) corresponding to the first and the last indexes of the minimalelements in the rows of array dy. One or both structures are used toderive the corresponding defect location. The location can be defined as(x,y) from defect structure 1320[j] of LDF structure 1205[i] where j andi are taken from either First or Last maxLDFGrad structure. In thedefault case the defect location is midway between these two. Theresulting list of defect locations 1230 accurately locates defectswithin line-type defect artifacts in the image.

The final step 1235 employs fiducial coordinates 1240 to translate theimage coordinates 1230 into physical defect coordinates 950 (FIG. 9).For this purpose, the object under test is assumed to be aligned withthe respective detector using fiducial marks, usually two of them.

The set of operations applied in DLA 945 is specific to line-typedefects in the example, but these operations can be easily modified asdesired to accommodate e.g. point- and corner-type defects.Defect-location algorithm 900 (FIG. 9) therefore locates various typesof defects within their respective artifacts.

While the present invention has been described in connection withspecific embodiments, variations of these embodiments will be obvious tothose of ordinary skill in the art. For example, the methods and systemsdescribed above can be applied to many types of electrical circuits,including integrated circuits, printed-circuit boards,micro-electromechanical systems (MEMS), semiconductor wafers, and somebiomedical specimens. Moreover, while the IR inspection systemsdescribed above employ a combination of electrical excitation andthermography that produces thermal defect artifacts, other types ofinspection systems employ other types of excitation and/or imaging, andconsequently produce other types of defect artifacts. For example, someimaging systems acquire images using white light, combined spectroscopy,or magnetic fields. The embodiments described above for isolatingdefects from defect artifacts are not limited to IR inspection systemsof the type described above.

Therefore, the spirit and scope of the appended claims should not belimited to the foregoing description.

1. An automated method of locating a defect on an object of interest,comprising: acquiring an image of the object, the image including adefect-artifact that contains the defect; classifying thedefect-artifact as a specific defect-artifact type, defect-artifact typeclassifications including a point-type, a line-type, and a corner-type;processing the image in accordance with an algorithm to locate thedefect within the defect artifact, the algorithm being adapted for thespecific defect-artifact type.
 2. The method of claim 1, furthercomprising: iteratively applying a first morphological filteringalgorithm to the image for each of i threshold levels, where i is aninteger greater than 1, thereby producing a set of i thresholded images.3. The method of claim 2, further comprising: applying a secondmorphological filtering algorithm to each of the i thresholded images,the second morphological filtering algorithm removing all types ofdefect-artifacts except for the specific defect-artifact type from thethresholded images.
 4. The method of claim 1, further comprising:filtering the image to remove all but one type of defect-artifact,thereby creating a type-specific image. 5-8. (canceled)
 9. An imagingsystem for localizing a defect on an object under test, the imagingsystem comprising: an excitation source for applying a test vector tothe defect, wherein the defect exhibits a response to the applied testvector; an image detector positioned to receive the response and producean image therefrom, the image including the defect and a defect-artifactencompassing a location of the defect; and means for classifying thedefect-artifact as a specific defect-artifact type, defect-artifact typeclassifications including a point-type, a line-type, and a corner-type,and for applying an algorithm to the image to locate the defect withinthe defect-artifact, the algorithm being adapted for the specificdefect-artifact type.
 10. The imaging system of claim 9, wherein theresponse is a thermal response.
 11. The imaging system of claim 9,wherein the means is further for iteratively applying a firstmorphological filtering algorithm to the image for each of i thresholdlevels, where i is an integer greater than 1, thereby producing a set ofi thresholded images wherein the set of i thresholded images includesthe defect-artifact.
 12. The imaging system of claim 11, wherein themeans is further for applying a second morphological filtering algorithmto each of the i thresholded images, the second morphological filteringalgorithm removing all types of defect-artifacts except for the specificdefect-artifact type.
 13. The imaging system of claim 12, wherein themeans generates a data structure associating type-specificdefect-artifacts from different type-specific images, the data structurelinking a tree of the thresholded defect-artifact images usingsuccessively greater threshold levels for gradient peak profiling, amaximum peak indicating the location of the defect.
 14. An imagingsystem for localizing a defect on an object under test, the imagingsystem comprising: an excitation source for applying a test vector tothe defect, wherein the defect exhibits a response to the applied totest vector; an image detector positioned to receive the response andproduce an image therefrom, the image including the defect anddefect-artifact encompassing a location of the defect; an imageprocessor operable to classify the defect-artifact as a specificdefect-artifact type, defect-artifact type classifications including apoint-type, a line-type, and a corner-type, the image processor beingfurther operable to apply a plurality of threshold values to the imageto produce a plurality of thresholded images wherein a number of thethresholded images includes the defect-artifact, the image processorbeing further operable to apply one or more morphological filteringalgorithms to each thresholded image to remove all of thedefect-artifacts except the specific defect-artifact type, the imageprocessor being further operable to establish a gradient between a pairof peak values of the defect-artifact of the specific defect-artifacttype.
 15. The imaging system of claim 14, wherein the image processor isfurther operable to calculate a location of the defect using thegradient.
 16. An inspection system comprising: an excitation source thatexcites an object under test object in a way that produces an imageresponse in an area of the object containing a defect; an infraredimaging device to capture the image response, the image responseincluding a defect response representative of the defect and adefect-artifact; and image-processing means for classifying thedefect-artifact as a specific defect-artifact type, defect-artifact typeclassifications including a point-type, a line-type, and a corner-type,and for processing the image response in accordance with an algorithm tolocate the defect, the algorithm being adapted for the specificdefect-artifact type.
 17. The inspection system of claim 16, wherein theimage-processing means is further for applying a plurality of thresholdvalues to the image response to produce a plurality of thresholdedimages, wherein a set of the thresholded images includes thedefect-artifact, and for filtering each thresholded image to remove allof the defect-artifacts except the specific defect-artifact type. 18.The inspection system of claim 17, wherein the image-processing means isfurther for calculating a gradient between a pair of threshold values ofthe defect-artifact of the specific defect-artifact data type, and forusing the gradient to locate the defect in the area of the object.
 19. Amethod comprising: acquiring an image of an object, the image includinga defect-artifact region that contains a defect; classifying thedefect-artifact region as a specific defect-artifact type; iterativelyapplying a first morphological filtering algorithm to the image for eachof i threshold levels, where i is an integer greater than 1, therebyproducing a set of i thresholded images; applying a second morphologicalfiltering algorithm to each of the i thresholded images to produce agrayscale composite image in which all types of defect-artifacts areremoved except for defect-artifacts of the specific defect-artifacttype; and profiling gradients of the grayscale composite image to locatepeak pixel intensities for each of the i filtered images, the defectbeing located a position of maximum gradient.
 20. The inspection systemof claim 19, wherein the i threshold levels of the defect-artifact ofthe specific defect-artifact data type, and for using the gradient tolocate the defect in the area of the object.
 21. The inspection systemof claim 19, wherein the defect-artifact type classifications includinga point-type.
 22. The inspection system of claim 19, wherein thedefect-artifact type classifications including a line-type.
 23. Theinspection system of claim 19, wherein the defect-artifact typeclassifications including a corner-type.